1

Supervisors :

Dr. : Howida AbdEl-fattah

2

Team members

• Mohamed Magdy Elsayed (CS)

• Mohamed Magdy Abd El-Rheem (CS)

• Mo’men Osama Abd El-Gaffar (CS)

• Mohamed Ahmed El-Sayed (CS)

• Mohamed Hamdy Ibraheem (CS)

3

Agenda• Problem Definition

• Project Objective

• Project Motivation

• System Architecture

• System implementation

• Future work

• Reference

4

Problem Definition

In year 1996 in Egypt:

• 1.6% Lose of one or both arms

• 3.2% Lose of one or both legs

• 18.7% Paralysis total or partial

5

Project Objective

Helping people Around the world to

overcome their disabilities and have

a normal life like any other one.

6

Project Motivation

• A lot of people cannot imagine how this system

will be done and used.

• This project not really popular in Egypt “till

now”.

• Recently, intense research has been conducted in

BCI technology

7

Project Motivation cont.

• And now many projects reach the levels of

success originally touted.

• We will deal with new technology and

implement it by using new techniques.

8

System architecture

9

Signal Acquisition

Preprocessing Feature Extraction

ClassificationDecision

System Acquisition

How to explore brain activity?

NoninvasiveInvasive

10

EMOTIV Headset

• The EMOTIV Headset (EPOC) has 14 electrodes(compared to the 19 electrodes of a standard medical EEG).

• We use only 5 channels (AF3-F7-F3-FC5-P7)

11

Preprocessing

• there are two purpose for preprocessing

Biological Environmental

Keep interest in EEG signals in certain frequency band(0.5-45)

Remove artifacts signals:

12

Band Pass Filter

Feature Extraction Techniques

1.Wavelet transformation (82%)

2.Fourier transformation(73%)

3.PCA (52%)

13

0

10

20

30

40

50

60

70

80

90

Wavlet Fourier PCA

Accuracy

Fourierprovides a signal which is localized

only in the Frequency domain.Features are magnitude values for the specified spectral range of frequencies

14

Ex: 1-Range(8-30) = 23 features for each channel2-Top Ten Frequencies for each channel

Wavelet packet decomposition WPD:

• Is localized in both time and frequency•Divided signal into component according to time•Parameters : according to the required Band and thesampling rate we select the number of levels for ourWPD•Features : Mu-Sigma-Min-Max-Epsilon (30 features foreach channel)

15

Principal Components Analysis

• It is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences

• The other main advantage of PCA is that once you have found these patterns in the data, and you compress the data without much loss of information.

• (5 features for each channel)

16

Classification

Neural Networks• A type of artificial intelligence that attempts to imitate the way a human

brain works. Rather than using a digital model.

In this step we need to classify the signal to detect the Arm motion

17

Multi-Layer Perceptron

18

Decision

Decision

19

System implementation

Demo

20

Future Work

• Add more movements of different parts of the body

• Get the data from emotive headset to the arm directly using wireless connection

• Implement the program on a microcontroller in the arm

21

References [1] R. Palaniappan and D. P. Mandic. EEG based biometric framework for automatic identity

verification. Journal of VLSI Signal ProcessingSystems, 49(2):243–250, 2007.

[2] R. Palaniappan and K. Ravi. Improving visual evoked potential feature classification for person recognition using PCA and normalization.

Pattern Recognition Letters, 27(7):726 – 733, 2006.[3] R. Paranjape, J. Mahovsky, L. Benedicenti, and Z. Koles’. The electroencephalogram as a

biometric. In Canadian Conference on Electrical and Computer Engineering, volume 2, pages 1363 –1366, 2001.

[4] M. Poulos, M. Rangoussi, V. Chrissikopoulos, and A. Evangelou. Parametric person identification from the EEG using computational

geometry. volume 2, pages 1005 –1008, Pafos, Cyprus, 1999.

[5] M. Poulos, M. Rangoussi, V. Chrissikopoulos, and A. Evangelou. Person identification based on parametric processing of the EEG. volume 1, pages 283 –286, Pafos, Cyprus, 1999.

22

Questions?

23

Thank YOU…

24